Deep shale gas reservoirs are characterized by deep burial, low permeability, heterogeneity and anisotropy, with pore sizes concentrated at micro- and nano-scale. The reconstruction of 3D digital cores based on local 2D scanning electron microscope(SEM) images have some problems, such as strong heterogeneity of pore distribution and poor representativeness of visual field. In this paper, we use the large-field splicing technology to obtain a large-field high-precision image by concatenating consecutive small-field high-precision images of shale gas reservoirs; Based on the large-field image, the Markov chain-Monte Carlo (MCMC) algorithm is adopted to reconstruct 3D digital cores of shale gas reservoirs. This algorithm is based on the conditional probability distribution constraints, which can better reflect the pore distribution and pore connectivity properties of the reservoir. Meanwhile, the large-field splicing technology also addresses the problem that small-field images are not representative of shale gas reservoirs.
KEYWORDS: 3D image reconstruction, Binary data, Digital imaging, 3D image processing, 3D modeling, Scanning electron microscopy, Electron microscopes, Visualization, Reconstruction algorithms, Monte Carlo methods
A deep shale gas reservoir has become a research hotspot in the field of unconventional oil and gas resources in recent years because of its huge reserves and development potential. In view of the characteristics of low permeability, heterogeneity and anisotropy of deep shale gas reservoirs, this paper adopts the Markov Chain-Monte Carlo (MCMC) algorithm based on statistical theory to reconstruct 3D digital cores of shale samples from deep shale gas reservoirs. Compared with traditional methods, such as statistical function, for testing the validity of MCMC algorithm, this paper verifies the validity of MCMC algorithm by calculating gas-water relative permeability curves of the reconstructed digital cores, where MCMC algorithm is applied to obtain different digital cores with similar gas–water relative permeability curves based on the same 2D scanning electron microscope image.
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